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Python [conda env:base] *
Kernel status: Idle
image/svg+xml
    [154]:
    #Importo la base de datos del banco nacion
    import wbdata
    import pandas as pd
    import os
    #Importo matplotlib.pyplot
    import matplotlib.pyplot as plt
    %matplotlib inline
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    from ISLP import load_data
    import statsmodels.api as sm

    from sklearn.preprocessing import scale
    from sklearn.linear_model import Lasso, LassoCV, Ridge, RidgeCV, ElasticNet, ElasticNetCV
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import mean_squared_error
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LinearRegression
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
    [162]:
    Selection deleted
    wbdata.get_countries(query='e')
    [162]:
    id    name
----  --------------------------------------------------------------------------------
AFE   Africa Eastern and Southern
AFW   Africa Western and Central
ARE   United Arab Emirates
ARG   Argentina
ARM   Armenia
ASM   American Samoa
AZE   Azerbaijan
BEA   East Asia & Pacific (IBRD-only countries)
BEC   Europe & Central Asia (IBRD-only countries)
BEL   Belgium
BEN   Benin
BGD   Bangladesh
BHI   IBRD countries classified as high income
BHS   Bahamas, The
BIH   Bosnia and Herzegovina
BLA   Latin America & the Caribbean (IBRD-only countries)
BLR   Belarus
BLZ   Belize
BMN   Middle East & North Africa (IBRD-only countries)
BMU   Bermuda
BRN   Brunei Darussalam
BSS   Sub-Saharan Africa (IBRD-only countries)
CAF   Central African Republic
CEA   East Asia and the Pacific (IFC classification)
CEB   Central Europe and the Baltics
CEU   Europe and Central Asia (IFC classification)
CHE   Switzerland
CHI   Channel Islands
CHL   Chile
CIV   Cote d'Ivoire
CLA   Latin America and the Caribbean (IFC classification)
CME   Middle East and North Africa (IFC classification)
CMR   Cameroon
COD   Congo, Dem. Rep.
COG   Congo, Rep.
CPV   Cabo Verde
CSS   Caribbean small states
CZE   Czechia
DEA   East Asia & Pacific (IDA-eligible countries)
DEC   Europe & Central Asia (IDA-eligible countries)
DEU   Germany
DLA   Latin America & the Caribbean (IDA-eligible countries)
DMN   Middle East & North Africa (IDA-eligible countries)
DNK   Denmark
DNS   IDA countries in Sub-Saharan Africa not classified as fragile situations
DOM   Dominican Republic
DSA   South Asia (IDA-eligible countries)
DSF   IDA countries in Sub-Saharan Africa classified as fragile situations
DSS   Sub-Saharan Africa (IDA-eligible countries)
DZA   Algeria
EAP   East Asia & Pacific (excluding high income)
EAR   Early-demographic dividend
EAS   East Asia & Pacific
ECA   Europe & Central Asia (excluding high income)
ECS   Europe & Central Asia
ECU   Ecuador
EGY   Egypt, Arab Rep.
EMU   Euro area
ERI   Eritrea
EST   Estonia
ETH   Ethiopia
EUU   European Union
FCS   Fragile and conflict affected situations
FRA   France
FRO   Faroe Islands
FSM   Micronesia, Fed. Sts.
FXS   IDA countries classified as fragile situations, excluding Sub-Saharan Africa
GBR   United Kingdom
GEO   Georgia
GIN   Guinea
GMB   Gambia, The
GNB   Guinea-Bissau
GNQ   Equatorial Guinea
GRC   Greece
GRD   Grenada
GRL   Greenland
GTM   Guatemala
HIC   High income
HPC   Heavily indebted poor countries (HIPC)
IBB   IBRD, including blend
IDB   IDA blend
IDN   Indonesia
IMN   Isle of Man
INX   Not classified
IRL   Ireland
IRN   Iran, Islamic Rep.
ISL   Iceland
ISR   Israel
KEN   Kenya
KGZ   Kyrgyz Republic
KNA   St. Kitts and Nevis
KOR   Korea, Rep.
LAC   Latin America & Caribbean (excluding high income)
LBN   Lebanon
LBR   Liberia
LCN   Latin America & Caribbean
LDC   Least developed countries: UN classification
LIC   Low income
LIE   Liechtenstein
LMC   Lower middle income
LMY   Low & middle income
LSO   Lesotho
LTE   Late-demographic dividend
LUX   Luxembourg
MAF   St. Martin (French part)
MDE   Middle East (developing only)
MDV   Maldives
MEA   Middle East & North Africa
MEX   Mexico
MIC   Middle income
MKD   North Macedonia
MNA   Middle East & North Africa (excluding high income)
MNE   Montenegro
MNP   Northern Mariana Islands
MOZ   Mozambique
NAC   North America
NCL   New Caledonia
NER   Niger
NGA   Nigeria
NLD   Netherlands
NPL   Nepal
NRS   Non-resource rich Sub-Saharan Africa countries
NXS   IDA countries not classified as fragile situations, excluding Sub-Saharan Africa
NZL   New Zealand
OED   OECD members
OSS   Other small states
PER   Peru
PHL   Philippines
PNG   Papua New Guinea
PRE   Pre-demographic dividend
PRI   Puerto Rico
PRK   Korea, Dem. People's Rep.
PSE   West Bank and Gaza
PSS   Pacific island small states
PST   Post-demographic dividend
PYF   French Polynesia
RRS   Resource rich Sub-Saharan Africa countries
RUS   Russian Federation
SEN   Senegal
SGP   Singapore
SLE   Sierra Leone
SLV   El Salvador
SRB   Serbia
SSA   Sub-Saharan Africa (excluding high income)
SST   Small states
STP   Sao Tome and Principe
SUR   Suriname
SVK   Slovak Republic
SVN   Slovenia
SWE   Sweden
SWZ   Eswatini
SXM   Sint Maarten (Dutch part)
SXZ   Sub-Saharan Africa excluding South Africa
SYC   Seychelles
SYR   Syrian Arab Republic
TEA   East Asia & Pacific (IDA & IBRD countries)
TEC   Europe & Central Asia (IDA & IBRD countries)
TKM   Turkmenistan
TLA   Latin America & the Caribbean (IDA & IBRD countries)
TLS   Timor-Leste
TMN   Middle East & North Africa (IDA & IBRD countries)
TSS   Sub-Saharan Africa (IDA & IBRD countries)
TUR   Turkiye
UKR   Ukraine
UMC   Upper middle income
USA   United States
UZB   Uzbekistan
VCT   St. Vincent and the Grenadines
VEN   Venezuela, RB
VNM   Viet Nam
XZN   Sub-Saharan Africa excluding South Africa and Nigeria
YEM   Yemen, Rep.
ZWE   Zimbabwe
    [166]:
    Selection deleted
    indicadores = {"HD.HCI.OVRL":"indice_capital_humano","SP.DYN.LE00.IN":"Esperanza_de_vida", "NY.GNP.PCAP.CD":"Ingreso_Percapita", "SE.SEC.ENRR":"Cumplimiento_Educacion_Secundaria", "HD.HCI.AMRT":"Supervivencia_15-60", "HD.HCI.MORT":"Probabilidad de sobrevivir a los 5"}

    df = wbdata.get_dataframe(indicadores, country=['ESP','GBR','FRA','BEL','NLD','DEU',"DNK","IRL","ISL",'MAR','NGA','DZA','COD','SEN','CMR',"KEN","GHA","EGY"])
    df = df.reset_index()
    df = df.dropna()
    df = df.sort_values(by='date')
    df
    [166]:
    country date indice_capital_humano Esperanza_de_vida Ingreso_Percapita Cumplimiento_Educacion_Secundaria Supervivencia_15-60 Probabilidad de sobrevivir a los 5
    50 Algeria 2010 0.531283 74.144000 4870.0 100.709122 0.895640 0.972591
    895 Netherlands 2010 0.797099 80.702439 53470.0 124.569527 0.936495 0.995537
    830 Morocco 2010 0.474435 70.821000 3190.0 63.267460 0.916093 0.967947
    1025 Senegal 2010 0.389767 64.221000 1340.0 36.972530 0.790471 0.933612
    700 Ireland 2010 0.766303 80.743902 44980.0 118.096909 0.929608 0.995826
    635 Iceland 2010 0.755178 81.897561 38020.0 108.819038 0.945691 0.997383
    1090 Spain 2010 0.708286 81.626829 31930.0 122.622513 0.935105 0.996145
    440 France 2010 0.756892 81.663415 43960.0 106.424942 0.917406 0.995725
    375 Egypt, Arab Rep. 2010 0.478853 69.078000 2200.0 65.257210 0.838623 0.971035
    505 Germany 2010 0.760740 79.987805 44650.0 104.891357 0.923014 0.995822
    115 Belgium 2010 0.752727 80.182927 47110.0 160.660477 0.919607 0.995539
    1155 United Kingdom 2010 0.765454 80.402439 41760.0 103.461151 0.926647 0.994842
    310 Denmark 2010 0.748738 79.100000 61290.0 118.944054 0.919474 0.995894
    180 Cameroon 2010 0.379783 56.965000 1450.0 44.039508 0.649568 0.892831
    317 Denmark 2017 0.774000 81.102439 56600.0 130.324432 0.931300 0.995700
    1162 United Kingdom 2017 0.781000 81.256098 41660.0 125.731430 0.936300 0.995700
    577 Ghana 2017 0.439000 63.829000 1830.0 66.381920 0.763000 0.950700
    187 Cameroon 2017 0.394000 60.817000 1410.0 50.637009 0.670900 0.916000
    707 Ireland 2017 0.806000 82.156098 53660.0 125.307121 0.945600 0.996500
    512 Germany 2017 0.795000 80.992683 43780.0 101.095642 0.931300 0.996300
    642 Iceland 2017 0.740000 82.660976 62450.0 115.741142 0.952400 0.997900
    447 France 2017 0.765000 82.575610 38300.0 103.756081 0.925600 0.995800
    122 Belgium 2017 0.757000 81.492683 42480.0 158.670288 0.929100 0.996200
    837 Morocco 2017 0.500000 73.608000 3160.0 78.536102 0.931600 0.976700
    382 Egypt, Arab Rep. 2017 0.486000 70.709000 2880.0 79.361198 0.853300 0.977900
    902 Netherlands 2017 0.800000 81.760976 46200.0 115.225227 0.944400 0.996100
    1097 Spain 2017 0.743000 83.282927 27120.0 120.555923 0.944500 0.996900
    708 Ireland 2018 0.813675 82.204878 59520.0 154.908295 0.939922 0.996267
    838 Morocco 2018 0.492541 73.946000 3380.0 79.016258 0.931606 0.976617
    123 Belgium 2018 0.762760 81.595122 45980.0 156.509766 0.929123 0.996235
    578 Ghana 2018 0.443490 64.138000 2060.0 67.753822 0.762185 0.950025
    1163 United Kingdom 2018 0.776982 81.256098 42020.0 120.190453 0.931713 0.995667
    513 Germany 2018 0.763773 80.892683 47540.0 100.837303 0.928101 0.996260
    903 Netherlands 2018 0.803039 81.812195 50480.0 114.500320 0.944422 0.996070
    448 France 2018 0.755960 82.675610 41130.0 103.773331 0.924162 0.995921
    188 Cameroon 2018 0.393321 61.189000 1520.0 46.472948 0.696741 0.921008
    1098 Spain 2018 0.736156 83.431707 29340.0 119.430199 0.944505 0.996882
    383 Egypt, Arab Rep. 2018 0.492512 70.974000 2710.0 79.986839 0.853568 0.978053
    318 Denmark 2018 0.770832 80.953659 61090.0 129.849564 0.929605 0.995741
    643 Iceland 2018 0.743431 82.860976 72600.0 114.649406 0.952734 0.997972
    968 Nigeria 2018 0.354753 52.669000 1950.0 42.168449 0.651890 0.877904
    1035 Senegal 2020 0.420111 67.496000 1440.0 46.348549 0.825251 0.956372
    970 Nigeria 2020 0.360610 53.072000 2060.0 45.889950 0.658540 0.880086
    1100 Spain 2020 0.728255 82.231707 27180.0 117.367767 0.946016 0.996966
    580 Ghana 2020 0.450056 64.309000 2250.0 73.274048 0.768254 0.952092
    840 Morocco 2020 0.504116 73.133000 3260.0 81.396782 0.934124 0.977589
    710 Ireland 2020 0.792599 82.456098 66310.0 135.224670 0.944305 0.996343
    645 Iceland 2020 0.745282 83.063415 65820.0 110.487320 0.954819 0.998038
    515 Germany 2020 0.751162 81.041463 48020.0 101.307800 0.930892 0.996340
    450 France 2020 0.762737 82.175610 39200.0 104.566566 0.926031 0.995956
    385 Egypt, Arab Rep. 2020 0.494375 69.790000 2960.0 83.162628 0.856794 0.978775
    320 Denmark 2020 0.755095 81.602439 62710.0 129.995193 0.931701 0.995777
    190 Cameroon 2020 0.397402 61.674000 1540.0 45.259029 0.704236 0.923940
    125 Belgium 2020 0.760420 80.695122 46090.0 151.727509 0.931235 0.996348
    905 Netherlands 2020 0.789915 81.358537 50030.0 136.284164 0.946277 0.996126
    1165 United Kingdom 2020 0.782943 80.331756 38770.0 114.651649 0.933412 0.995742
    [200]:
    count mean std min 25% 50% 75% max
    Esperanza_de_vida 25.0 1.030287e-15 1.020621 -2.619058 -0.290509 0.530714 0.605304 0.801949
    Ingreso_Percapita 25.0 3.552714e-17 1.020621 -1.431371 -1.334923 0.567481 0.793971 1.203061
    Cumplimiento_Educacion_Secundaria 25.0 3.996803e-16 1.020621 -1.699291 -0.593887 0.173205 0.672207 1.841811
    Supervivencia_15-60 25.0 7.904788e-16 1.020621 -2.406163 0.333213 0.442597 0.479543 0.628496
    Probabilidad de sobrevivir a los 5 25.0 1.176836e-16 1.020621 -2.831670 -0.004022 0.545472 0.556930 0.578859 
    Coeficiente de determinación: 0.9642260983852513

Intercepto: 0.22673553927021511

Coeficientes: [ 1.81261704e-02  1.94590172e-06  6.61194071e-04 -4.85421320e-01
 -6.64697653e-01]

    Respuesta predicha:
[0.56550497 0.75964777 0.47040559 0.41358855 0.74275063 0.73514169
 0.73347204 0.75570526 0.47375359 0.72287512 0.76991453 0.7227092
 0.75012439 0.3824561  0.77920762 0.74745712 0.42885868 0.43080724
 0.78179477 0.73254698 0.79749434 0.75543416 0.77828281 0.51761465
 0.50227843 0.75429859 0.74770225 0.81656543 0.52453904 0.78548721
 0.43665829 0.7467424  0.73945995 0.76308554 0.76338275 0.41913856
 0.75398408 0.50693309 0.78572992 0.81993848 0.31311644 0.44733437
 0.31841762 0.72587686 0.43945783 0.50927483 0.81913888 0.80660665
 0.74199362 0.75015829 0.48601235 0.79969712 0.42157861 0.7651251
 0.76745225 0.7191269 ]
    [207]:
    # Especificamos el modelo
    model = sm.OLS(y, x)
    # Ajustamos el modelo
    results = model.fit()

    print(results.summary())
                                      OLS Regression Results                                  
==========================================================================================
Dep. Variable:     indice_capital_humano   R-squared (uncentered):                   0.998
Model:                               OLS   Adj. R-squared (uncentered):              0.998
Method:                    Least Squares   F-statistic:                              5053.
Date:                   Sat, 17 May 2025   Prob (F-statistic):                    1.88e-67
Time:                           19:19:06   Log-Likelihood:                          116.79
No. Observations:                     56   AIC:                                     -223.6
Df Residuals:                         51   BIC:                                     -213.4
Df Model:                              5                                                  
Covariance Type:               nonrobust                                                  
======================================================================================================
                                         coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------------------------
Esperanza_de_vida                      0.0174      0.003      6.831      0.000       0.012       0.022
Ingreso_Percapita                   1.999e-06   3.88e-07      5.151      0.000    1.22e-06    2.78e-06
Cumplimiento_Educacion_Secundaria      0.0006      0.000      2.302      0.025    8.17e-05       0.001
Supervivencia_15-60                   -0.5274      0.180     -2.932      0.005      -0.889      -0.166
Probabilidad de sobrevivir a los 5    -0.3354      0.093     -3.619      0.001      -0.522      -0.149
==============================================================================
Omnibus:                        0.013   Durbin-Watson:                   2.115
Prob(Omnibus):                  0.994   Jarque-Bera (JB):                0.080
Skew:                          -0.025   Prob(JB):                        0.961
Kurtosis:                       2.822   Cond. No.                     1.68e+06
==============================================================================

Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[3] The condition number is large, 1.68e+06. This might indicate that there are
strong multicollinearity or other numerical problems.
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